通过联盟游戏理论对CNN可解释性的互动探索.
Lei Yang1, Lingmeng Lu1, Chao Liu2
1School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Scientific reports
|March 19, 2025
概括
本研究介绍了NeuronIM和LayerIM指标,以有效地解释卷积神经网络 (CNN). 这些指标量化了神经元和层的解释性,改善了CNN分析和模型修剪.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 对于图像分类至关重要.
- 神经元特征可视化有助于理解CNN机制.
- 目前用于CNN解释性的方法通常是低效的,因为神经元的数量很大.
研究的目的:
- 在CNN中开发高效的指标来评估神经元和层解释性.
- 为了加强CNN机制的探索和确定重要的神经元.
- 为改进CNN解释性分析创建交互式接口.
主要方法:
- 建议的神经元解释度量 (NeuronIM) 灵感来自夏普利添加式扩展 (SHAP).
- NeuronIM通过将神经元可视化与SHAP图像进行比较来量化特征表达能力.
- 通过对卷积层的NeuronIM值进行平均计算,引入了层解释度量 (LayerIM).
- 开发了两个交互式接口,用于多视图显示和用户交互.
主要成果:
- NeuronIM有效地评估个体神经元的特征表达.
- LayerIM为卷积层的解释性提供了一种定量度量.
- 交互式接口促进了对CNN可解释性的高效探索.
- 通过模型修剪实验和用例证明的有效性.
结论:
- NeuronIM和LayerIM显著提高了CNN解释性探索的效率.
- 提出的指标和接口可以快速识别重要的神经元和层.
- 这项工作为理解和优化CNN模型提供了一种实用方法.
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